NDLI: A fast Incremental Kernel Principal Component Analysis for data streams

Content Provider	IEEE Xplore Digital Library
Author	Anak Joseph, A. Ozawa, S.
Copyright Year	2014
Description	Author affiliation: Grad. Sch. of Eng., Kobe Univ., Kobe, Japan (Anak Joseph, A.; Ozawa, S.)
Abstract	Kernel Principal Component Analysis (KPCA) is widely used feature extraction as it have been proven that KPCA is powerful in many areas in pattern recognition. Considering that the conventional KPCA should decompose a kernel matrix of all training data, this would be an unrealistic assumption for data streams in real-world applications. Therefore, in this paper, we propose an online feature extraction called Chunk Incremental Kernel Principal Component Analysis (CIKPCA) that can handle data streams in an incremental mode. In the proposed method, the training data are assumed to be given in a chunk of multiple data at one time. In CIKPCA, an eigen-feature space is updated by solving the eigenvalue decomposition once whenever a chunk of data is given. However, if a chunk size is large, a kernel matrix to be decomposed is also large, resulting in high computational time. Considering that not all the data are useful for the eigen-feature space learning, the data in a chunk are first selected based on the importance. Several benchmark data sets in the UCI Machine Learning Repository are used to evaluate the performance of the proposed method. The experimental results show that our proposed method can accelerate the learning of the eigen-feature space compared to Takeuchi et al.'s IKPCA without reducing the recognition accuracy.
Starting Page	3135
Ending Page	3142
File Size	573450
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889940
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Training data Eigenvalues and eigenfunctions Principal component analysis Accuracy Matrix decomposition Feature extraction
Content Type	Text
Resource Type	Article

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